1. IEEE 802.16 Presentation Submission Template (Rev. 9) Document Number:
Trace Based Streaming Video Traffic Model for m Evaluation Methodology Document
IEEE Presentation Submission Template (Rev. 9)
Document Number:
IEEES80216m-07_179
Date Submitted:
Source:
Ricardo Fricks, Hua Xu
Motorola Inc.
1501 West Shure Drive, Arlington Heights, IL 60004, USA
Ronny (Yong-Ho) Kim, Kiseon Ryu
LG Electronics Inc.
LG R&D Complex, 533 Hogye-1dong, Dongan-gu Anyang, , Korea
Venue:
Response to a call for comments and contributions on draft m Evaluation Methodology Document
Base Contribution:
None
Purpose:
Propose to adapt the trace based streaming video traffic model as mandatory model in draft IEEE m Evaluation Methodology Document.
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2. Propose to adapt trace based streaming video model as MANDATORY model (1)
Trace-based streaming video traffic model represents the key characteristics of streaming video application better than the proposed statistical models
Traffic model in the evaluation methodology document (EVM) should focus on capturing the accents of the application which posts special demand on the system performance,
In the streaming video traffic model case, the long rang dependency (LRD) is the key characteristic that needs to be captured, because high burstiness resulting from LRD posts high demand on both transport and buffering capability in the system
The statistical models proposed in EVM don’t exhibits the LRD characteristic of encoded video traces. (See slides 4-6)
Trace-based streaming video traffic model can be easily used for design comparison
The trace based streaming video traffic model can be easily duplicated by other system simulators for design comparison
No additional statistical variance is introduced by the trace based traffic model itself, which make it easier to compare among results

3. Propose to adapt trace based streaming video model as MANDATORY model (2)
Trace-based streaming video traffic model is widely available and easy to implement
Trace based streaming video traffic model an be easily generated by every system simulator (simply read in the trace text file) with no ambiguity
It exhibits LRD in an hour trace where large sample size (over 100K samples) will be needed for a statistical model to exhibit LRD
There is a public library with 200+ video traces with multiple encoding characteristics (e.g., video quality and resolution) and from diverse video sources (e.g., movies, TV shows, teleconferences, remote learning, cartoons, sporting events).
Trace-based steaming video traffic model can represent the general population of the streaming video
The trace based streaming video model consists of 12 traces carefully picked from five genre with different data rate, quantization value, hurst parameters, etc.
Users (in the simulation) shall pick a trace randomly from the 12 candidates
Further more, a random starting point in the trace shall also be picked by the user

4. Effect of LRD on Network Performance
Network performance degrades gradually with increasing LRD (self-similarity).
The more self-similar the traffic, the slower the queue length decays.
Aggregating streams of self-similar traffic typically intensifies the self-similarity ("burstiness") rather than smoothing it.
The bursty behaviour may itself be bursty, which exacerbates the clustering phenomena, and degrades network performance.
QoS depends on coping with traffic peaks - video delay bound may be exceeded.

5. Old school surveys:
New modeling trend using traces:

6. 802.16m streaming video model:
AR(2)
... but long-range dependence is intrinsic to VBR encoded streams
and there is evidence that LRD processes can negatively affect multiplexing performance

7. Long-Range Dependence
Silence of the Lambs
Besides the statistical characteristics of the same video stream will depend of many factors: GoP size, quantization, etc
Long-Range Dependence

8. Long-Range Dependence
Properties: Encoder: H.264 Full Variable Bit Rate (VBR) Frame Size: CIF 352x288 GoP Size: 16 No. B Frames: 1 Quantizer: 10
Long-Range Dependence
Properties: Encoder: H.264 Full Variable Bit Rate (VBR) Frame Size: CIF 352x288 GoP Size: 16 No. B Frames: 1 Quantizer: 16
Long-Range Dependence

9. Long-Range Dependence
Properties: Encoder: H.264 Full Variable Bit Rate (VBR) Frame Size: CIF 352x288 GoP Size: 16 No. B Frames: 1 Quantizer: 48
Long-Range Dependence
Short-Range Dependence (?)
Properties:
Encoder: MPEG4 Rel2
Variable Bit Rate (VBR)
Frame Size: (?) 320x240
GoP Size: 12
No. B Frames: 8
Quantizer: (?)